首页|Stanford University Reports Findings in Machine Learning (Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance)
Stanford University Reports Findings in Machine Learning (Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Stanford, California, by NewsRx journalists, research stated, “Computationally predicting the performanc e of catalysts under reaction conditions is a challenging task due to the comple xity of catalytic surfaces and their evolution in situ, different reaction paths , and the presence of solid-liquid interfaces in the case of electrochemistry. W e demonstrate here how relatively simple machine learning models can be found th at enable prediction of experimentally observed onset potentials.” The news correspondents obtained a quote from the research from Stanford Univers ity, “Inputs to our model are comprised of data from the oxygen reduction reacti on on non-precious transition-metal antimony oxide nanoparticulate catalysts wit h a combination of experimental conditions and computationally affordable bulk a tomic and electronic structural descriptors from density functional theory simul ations. From human-interpretable genetic programming models, we identify key exp erimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. We finally v alidate these machine learning predictions by experimentally confirming that sca ndium as a dopant in nickel antimony oxide leads to a desired onset potential in crease.”
StanfordCaliforniaUnited StatesNor th and Central AmericaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
